The Fin AI
Purpose
The rapid advancement of AI in the financial sector brings both opportunities and challenges, particularly in the development and deployment of Large Language Models (LLMs). The Fin AI working group is established to spearhead open science, tooling, and model initiatives, ensuring responsible innovation and application in financial services.
Open Science
Financial AI Research
Open Tooling
Financial AI Development & Analysis
Open Models
Financial AI Application
Key Projects
Our key projects comprehensively cover the lifecycle of financial LLMs, from curating datasets and developing benchmarks to releasing models, creating application frameworks, and optimizing deployment systems. These efforts ensure innovation, transparency, and ethical standards in financial AI.
FinData
Curating comprehensive open-source datasets for pre-training, fine-tuning, and evaluating financial LLMs, addressing the need for diverse and robust data resources.
FinBen
Developing holistic benchmarks incorporating instruction-based, multi-turn, multi-modal, multi-lingual, Retrieval-Augmented Generation (RAG) based, and agent-based evaluations to accurately assess financial LLM performance.
Papers:
The FinBen: An Holistic Financial Benchmark for Large Language Models
PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance
DĂłlares or Dollars? Unraveling the Bilingual Prowess of Financial LLMs Between Spanish and EnglishÂ
Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models
Project: https://github.com/The-FinAI/PIXIUÂ
FinModel
Releasing a series of open-source financial LLMs including 1.5B, 8B, and 70B parameter models along with their checkpoints and training scripts, facilitating accessibility and innovation in model development.
Papers:
1. Open-FinLLMs: Open Multimodal Large Language Models for Financial Applications
FinCon
Building a multi-agent collaborative system equipped with human-like cognitive capabilities and quantified dynamic risk profiles, designed to optimize the quality of sequential, multi-turn financial decision-making processes. This innovative system leverages multi-modal financial data inputs, forging a robust link between intelligent system design and behavioral finance principles. It is versatile enough to be employed in a wide range of financial tasks.
Papers:
FinApp
Creating a federated learning-based application framework for reliable and trustworthy financial LLM applications, emphasizing privacy and security in financial AI deployments.
Papers:
Committee Member
Jimin Huang
The Fin AI
CEO & COO
Qianqian Xie
Yale University
Associate Research Scientist
Sophia Ananiadou
The University of Manchester
Archimedes AI Research Unit
Professor
Min Peng
Wuhan University,
Professor
Jian-Yun Nie
University of Montreal
Professor
Ion Androutsopoulos
Athens University of Economics and Business
Archimedes AI Research Unit
Professor
Alejandro Lopez-lira
University of Florida
Assistant Professor
Steve Yang
Stevens Institute of Technology
Center for Research toward Advancing Financial Technologies (CRAFT)
Associate Professor
Benyou Wang
Chinese University of Hong Kong, Shenzhen
Assistant Professor
Eddie Zhao
University of Massachusetts Boston
Associate Professor
Jordan Suchow
Stevens Institute of Technology
Assistant Professor
Arman Cohan
Yale University
Assistant Professor
Hao Wang
Sichuan University
Associate Professor
Jiajia Huang
Nanjing Audit University
Associate Professor
Yanzhao Lai
Southwest Jiaotong University
Assitant Professor
Gang Hu
Yunnan Univeristy
Lecturer
Kaleb Smith
NVIDIA Higher Education Research (HER) - Senior Data Scientist
NVIDIA AI Technology Center (NVAITC) - NALA HER Lead